Neighbourhood · North District

Nanya & Guanghua

湳雅・光華

Homes along Nanya Street and the Guanghua redevelopment area

Compared with the 9 Hsinchu neighbourhoods in this guide (scores 0–10)

Street Environment6.3No. 3

Cleanliness7.9No. 3

Buildings5.7No. 3

Sidewalks6.0No. 4

Greenery5.7No. 3

Convenience5.7No. 5

Transit3.2No. 7

Everyday shopping7.5No. 3

Healthcare8.4No. 4

Nearby neighbourhoods

The closest neighbourhoods in the same city.

Beimen Elementary / Cultural Affairs BureauHomes around the Cultural Bureau and Beimen school NanliaoFishing harbour, windmills and the 17 km coastline Old Town: East Gate & City God TempleStation, East Gate, moat and temple-front food: the old-town core Niupu & Xiangshan District OfficeHomes around the district office and Dingpu school NTHU & NYCUThe Jiangong blocks and night market between the two campuses

72 blocks · Official boundary · Data version 2026-09-24

How are these measured?

Compared against the Hsinchu neighbourhoods in this guide; metrics without enough data are not ranked. Shopping counts convenience stores plus the nearer of a supermarket or a traditional market (public and private retail markets); night markets, vendor zones and shopping-street shops are not counted. Transit is rail-first with a lower weight for buses.

TidyMap rates the street environment and everyday convenience. It does not assess the home itself, safety, noise or actual living experience. About data limits

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The apartment looks good. What about the street? TidyMap uses AI street imagery analysis and public facility data to turn nearby convenience and the street environment into something you can compare. Search an address you're considering to see the neighborhood around it.

Street-level samplingOne street-level photo every 50 m along each street (each grid shows its capture date and source)
AI scoringAn AI vision model rates cleanliness, buildings, sidewalks and greenery; their average is the street environment score
Plus everyday convenienceAccess to transit, shopping and healthcare from public facility data, combined with the street environment into the overall score (weights on the methodology page)

How to read it: yellow = poor, blue = good; grey = not enough data

TidyMap rates the street environment and everyday convenience. It does not assess the home itself, safety, noise or actual living experience. Street environment scores use AI assessments of imagery; convenience scores use public facility data.

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Nanya & Guanghua

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